Authors: Xiao Chen, Shunan Zhang, Eric Z. Chen, Yikang Liu, Lin Zhao, Terrence Chen, Shanhui Sun

Published on: March 13, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2403.08887

Summary

• What is new: Introduction of the Federated Data Model (FDM) that uses diffusion models for creating synthetic data for privacy-respecting AI model training across different locations.
• Why this is important: Challenges in training deep learning models due to data privacy, regulations, and issues in sharing data across locations, particularly for medical applications.
• What the research proposes: The FDM method that learns data characteristics from one site to create synthetic data for use at another site without sharing actual data.
• Results: Models trained with FDM showed effective performance on both original and other sites’ data, proving its potential in privacy-respecting AI model training.

Technical Details

Technological frameworks used: Federated Data Model (FDM)

Models used: Diffusion models

Data used: Cardiac magnetic resonance images from different hospitals

Potential Impact

Healthcare AI applications, medical imaging companies, and data privacy-focused technology sectors.

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